The recommendation has been playing a key role in many industries, e.g.,
e-commerce, streaming media, social media, etc. Recently, a new recommendation
scenario, called Trigger-Induced Recommendation (TIR), where users are able to
explicitly express their instant interests via trigger items, is emerging as an
essential role in many e-commerce platforms, e.g., Alibaba.com and Amazon.
Without explicitly modeling the user's instant interest, traditional
recommendation methods usually obtain sub-optimal results in TIR. Even though
there are a few methods considering the trigger and target items simultaneously
to solve this problem, they still haven't taken into account temporal
information of user behaviors, the dynamic change of user instant interest when
the user scrolls down and the interactions between the trigger and target
items. To tackle these problems, we propose a novel method -- Deep Evolutional
Instant Interest Network (DEI2N), for click-through rate prediction in TIR
scenarios. Specifically, we design a User Instant Interest Modeling Layer to
predict the dynamic change of the intensity of instant interest when the user
scrolls down. Temporal information is utilized in user behavior modeling.
Moreover, an Interaction Layer is introduced to learn better interactions
between the trigger and target items. We evaluate our method on several offline
and real-world industrial datasets. Experimental results show that our proposed
DEI2N outperforms state-of-the-art baselines. In addition, online A/B testing
demonstrates the superiority over the existing baseline in real-world
production environments.

通过设计用户即时兴趣模型层以预测用户滚动时即时兴趣强度的动态变化，利用时间信息进行用户行为建模，引入交互层以学习更好的触发器和目标项之间的交互，我们提出了一种新颖的方法 —— 深度演化即时兴趣网络（DEI2N），用于触发诱导推荐场景中的点击率预测。实验结果表明，我们的 DEI2N 方法在离线数据集和真实世界工业数据集上胜过现有的基准方法，而在线 A/B 测试则证明了其在实际生产环境中的优越性。